{"product_id":"generative-artificial-intelligence-concepts-and-applications-hardback-9781394209224","title":"Generative Artificial Intelligence; Concepts and Applications (Hardback) 9781394209224","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eGenerative Artificial Intelligence\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eConcepts and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eR. Nidhya (Edited by), R. Nidhya (Author), D. Pavithra (Edited by), Manish Kumar (Edited by), A. Dinesh Kumar (Edited by), S. Balamurugan (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394209224, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 21 May 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e304 pages\u003cbr\u003e22.9 x 15.2 x 2 cm, 0.68 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThis book is a comprehensive overview of AI fundamentals and applications to drive creativity, innovation, and industry transformation.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eGenerative AI stands at the forefront of artificial intelligence innovation, redefining the capabilities of machines to create, imagine, and innovate. GAI explores the domain of creative production with new and original content across various forms, including images, text, music, and more. In essence, generative AI stands as evidence of the boundless potential of artificial intelligence, transforming industries, sparking creativity, and challenging conventional paradigms. It represents not just a technological advancement but a catalyst for reimagining how machines and humans collaborate, innovate, and shape the future. \u003c\/p\u003e\n\u003cp\u003eThe book examines real-world examples of how generative AI is being used in a variety of industries. The first section explores the fundamental concepts and ethical considerations of generative AI. In addition, the section also introduces machine learning algorithms and natural language processing. The second section introduces novel neural network designs and convolutional neural networks, providing dependable and precise methods. The third section explores the latest learning-based methodologies to help researchers and farmers choose optimal algorithms for specific crop and hardware needs. Furthermore, this section evaluates significant advancements in revolutionizing online content analysis, offering real-time insights into content creation for more interactive processes. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e\u003cbr\u003e The book will be read by researchers, engineers, and students working in artificial intelligence, computer science, and electronics and communication engineering as well as industry application areas.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Exploring the Creative Frontiers: Generative AI Unveiled 1\u003cbr\u003e \u003c\/b\u003eGenerated Using ChatGPT\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.1.1 Definition and Significance of Generative AI 1\u003c\/p\u003e \u003cp\u003e1.1.2 Historical Overview and Development 2\u003c\/p\u003e \u003cp\u003e1.2 Foundational Concepts 4\u003c\/p\u003e \u003cp\u003e1.2.1 Neural Networks and Generative Models 4\u003c\/p\u003e \u003cp\u003e1.2.2 Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) 5\u003c\/p\u003e \u003cp\u003e1.3 Applications Across Domains 7\u003c\/p\u003e \u003cp\u003e1.3.1 Creative Arts: Music, Visual Arts, Literature 7\u003c\/p\u003e \u003cp\u003e1.3.2 Content Generation: Text, Images, Videos 8\u003c\/p\u003e \u003cp\u003e1.3.3 Scientific Research and Data Augmentation 9\u003c\/p\u003e \u003cp\u003e1.3.4 Healthcare and Drug Discovery 10\u003c\/p\u003e \u003cp\u003e1.3.5 Gaming and Virtual Environments 12\u003c\/p\u003e \u003cp\u003e1.4 Ethical Considerations 13\u003c\/p\u003e \u003cp\u003e1.5 Future Prospects and Challenges 15\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 16\u003c\/p\u003e \u003cp\u003eReference 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 An Efficient Infant Cry Detection System Using Machine Learning and Neuro Computing Algorithms 19\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSwarna Kuchibhotla, Kantheti Mohana, Alapati Yomitha, Sruthi Yedavalli, Hima Deepthi Vankayalapati and Kyamakya Kyandoghere\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 20\u003c\/p\u003e \u003cp\u003e2.2 Literature Survey 21\u003c\/p\u003e \u003cp\u003e2.3 Methodology 23\u003c\/p\u003e \u003cp\u003e2.3.1 Database 24\u003c\/p\u003e \u003cp\u003e2.3.2 Feature Extraction 25\u003c\/p\u003e \u003cp\u003e2.3.2.1 Short-Term Energy 25\u003c\/p\u003e \u003cp\u003e2.3.2.2 Mel-Frequency Cepstral Coefficients 26\u003c\/p\u003e \u003cp\u003e2.3.2.3 Spectrograms 27\u003c\/p\u003e \u003cp\u003e2.3.3 Classification 29\u003c\/p\u003e \u003cp\u003e2.3.4 Convolutional Neural Network (CNN) 29\u003c\/p\u003e \u003cp\u003e2.3.5 Recurrent Neural Network (RNN) 31\u003c\/p\u003e \u003cp\u003e2.3.6 Regularized Discriminant Analysis (RDA) 31\u003c\/p\u003e \u003cp\u003e2.3.7 Multi-Layer Perceptron (MLP) 33\u003c\/p\u003e \u003cp\u003e2.4 Experimental Results 33\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 35\u003c\/p\u003e \u003cp\u003eReferences 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Improved Brain Tumor Segmentation Utilizing a Layered CNN Model 39\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBilal Hikmat Rasheed and P. Sudhakaran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 40\u003c\/p\u003e \u003cp\u003e3.2 Related Works 41\u003c\/p\u003e \u003cp\u003e3.3 Methodology 42\u003c\/p\u003e \u003cp\u003e3.4 Numerical Results 45\u003c\/p\u003e \u003cp\u003e3.5 Conclusion 49\u003c\/p\u003e \u003cp\u003eReferences 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Natural Language Processing in Generative Adversarial Network 53\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eP. Dhivya, A. Karthikeyan, S. Pradeep and H. Umamaheswari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 54\u003c\/p\u003e \u003cp\u003e4.2 Literature Survey 57\u003c\/p\u003e \u003cp\u003e4.3 The Implementation of NLP in GAN for Generating Images and Summaries 61\u003c\/p\u003e \u003cp\u003e4.3.1 Working of Sequence Generative Adversarial Network (SeqGAN) 61\u003c\/p\u003e \u003cp\u003e4.3.2 Working of Generative Adversarial Transformer (GAT) 63\u003c\/p\u003e \u003cp\u003e4.3.2.1 Steps to Incorporate NLP in GAN 64\u003c\/p\u003e \u003cp\u003e4.3.3 Implementation of NLP in GAN 65\u003c\/p\u003e \u003cp\u003e4.3.4 Generate the Image Using Textual Description 68\u003c\/p\u003e \u003cp\u003e4.3.5 Text Summarization 69\u003c\/p\u003e \u003cp\u003e4.3.5.1 Graph-Based Summarization 71\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 77\u003c\/p\u003e \u003cp\u003eReferences 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Modeling A Deep Learning Network Model for Medical Image Panoptic Segmentation 81\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJyothsna Devi Koppagiri and Gouranga Mandal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 81\u003c\/p\u003e \u003cp\u003e5.2 Related Works 84\u003c\/p\u003e \u003cp\u003e5.3 Methodology 85\u003c\/p\u003e \u003cp\u003e5.3.1 Deep Masking Convolutional Model (DMCM) 85\u003c\/p\u003e \u003cp\u003e5.4 Numerical Results and Discussion 87\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 91\u003c\/p\u003e \u003cp\u003eReferences 91\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 A Hybrid DenseNet Model for Dental Image Segmentation Using Modern Learning Approaches 93\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePulipati Nagaraju and S. V. Sudha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 94\u003c\/p\u003e \u003cp\u003e6.2 Related Works 95\u003c\/p\u003e \u003cp\u003e6.3 Methodology 96\u003c\/p\u003e \u003cp\u003e6.3.1 Dataset 96\u003c\/p\u003e \u003cp\u003e6.3.2 Dense Transformer Model 97\u003c\/p\u003e \u003cp\u003e6.3.3 DenseNet Model 100\u003c\/p\u003e \u003cp\u003e6.4 Numerical Results and Discussion 100\u003c\/p\u003e \u003cp\u003e6.4.1 Discussion 103\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 104\u003c\/p\u003e \u003cp\u003eReferences 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Modeling A Two-Tier Network Model for Unconstraint Video Analysis Using Deep Learning 107\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eP. Naga Bhushanam and Selva Kumar S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 108\u003c\/p\u003e \u003cp\u003e7.2 Related Works 109\u003c\/p\u003e \u003cp\u003e7.3 Methodology 110\u003c\/p\u003e \u003cp\u003e7.4 Numerical Results and Discussion 113\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 117\u003c\/p\u003e \u003cp\u003eReferences 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Detection of Peripheral Blood Smear Malarial Parasitic Microscopic Images Utilizing Convolutional Neural Network 121\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTamal Kumar Kundu, Smritilekha Das and R. Nidhya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 122\u003c\/p\u003e \u003cp\u003e8.2 Malaria 124\u003c\/p\u003e \u003cp\u003e8.2.1 Malaria-Infected Red Blood Cells with Types 124\u003c\/p\u003e \u003cp\u003e8.3 Literature Survey 125\u003c\/p\u003e \u003cp\u003e8.4 Proposed Methodology and Algorithm 130\u003c\/p\u003e \u003cp\u003e8.4.1 Proposed Algorithm 135\u003c\/p\u003e \u003cp\u003e8.5 Result Analysis 135\u003c\/p\u003e \u003cp\u003e8.5.1 Dataset 135\u003c\/p\u003e \u003cp\u003e8.5.2 Preprocessing of Data 135\u003c\/p\u003e \u003cp\u003e8.5.3 Splitting of Dataset 137\u003c\/p\u003e \u003cp\u003e8.5.4 Classification 137\u003c\/p\u003e \u003cp\u003e8.5.5 Model Prediction and Performance Metrics 137\u003c\/p\u003e \u003cp\u003e8.5.6 CNN Learning Curves 138\u003c\/p\u003e \u003cp\u003e8.6 Discussion 139\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 139\u003c\/p\u003e \u003cp\u003e8.8 Future Scope 139\u003c\/p\u003e \u003cp\u003eReferences 140\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Exploring the Efficacy of Generative AI in Constructing Dynamic Predictive Models for Cybersecurity Threats: A Research Perspective 143\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eT. Manasa and K. Padmanaban\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 144\u003c\/p\u003e \u003cp\u003e9.2 Related Works 145\u003c\/p\u003e \u003cp\u003e9.3 Methodology 146\u003c\/p\u003e \u003cp\u003e9.3.1 Pre-Processing 147\u003c\/p\u003e \u003cp\u003e9.3.2 Classifier 147\u003c\/p\u003e \u003cp\u003e9.3.3 Optimization 148\u003c\/p\u003e \u003cp\u003e9.4 Numerical Results and Discussion 149\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 152\u003c\/p\u003e \u003cp\u003eReferences 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Poultry Disease Detection: A Comparative Analysis of CNN, SVM, and YOLO v3 Algorithms for Accurate Diagnosis 155\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSpoorthi Shetty and Mangala Shetty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 156\u003c\/p\u003e \u003cp\u003e10.2 Literature Review 157\u003c\/p\u003e \u003cp\u003e10.3 Objectives 158\u003c\/p\u003e \u003cp\u003e10.3.1 Accurate Disease and Early Disease Identification 158\u003c\/p\u003e \u003cp\u003e10.3.2 Multi-Class Disease Identification 158\u003c\/p\u003e \u003cp\u003e10.3.3 Automation and Real-Time Disease Monitoring 159\u003c\/p\u003e \u003cp\u003e10.3.4 Better Accuracy 159\u003c\/p\u003e \u003cp\u003e10.4 Methodology 159\u003c\/p\u003e \u003cp\u003e10.4.1 Dataset 159\u003c\/p\u003e \u003cp\u003e10.4.2 Data Preprocessing 160\u003c\/p\u003e \u003cp\u003e10.4.3 Image Preprocessing 161\u003c\/p\u003e \u003cp\u003e10.4.4 Data Augmentation 161\u003c\/p\u003e \u003cp\u003e10.4.5 Extracting Region of Interest 162\u003c\/p\u003e \u003cp\u003e10.5 Results and Discussion 165\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 169\u003c\/p\u003e \u003cp\u003eReferences 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Generative AI-Enhanced Deep Learning Model for Crop Type Analysis Based on Clustered Feature Vectors and Remote Sensing Imagery 173\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eB. Bazeer Ahamed, D. Yuvaraj and Saif Saad Alnuaimi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 174\u003c\/p\u003e \u003cp\u003e11.2 Related Works 176\u003c\/p\u003e \u003cp\u003e11.3 Methodology 178\u003c\/p\u003e \u003cp\u003e11.3.1 Saliency Analysis 180\u003c\/p\u003e \u003cp\u003e11.3.2 Saliency Region Analysis with Belief Networking 181\u003c\/p\u003e \u003cp\u003e11.3.3 Group Analysis 182\u003c\/p\u003e \u003cp\u003e11.3.4 Classification 183\u003c\/p\u003e \u003cp\u003e11.3.5 Parameter Setup 183\u003c\/p\u003e \u003cp\u003e11.4 Numerical Results and Discussion 184\u003c\/p\u003e \u003cp\u003e11.4.1 Dataset 186\u003c\/p\u003e \u003cp\u003e11.4.2 Classification Results and Discussions 187\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 190\u003c\/p\u003e \u003cp\u003eReferences 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Cardiovascular Disease Prediction with Machine Learning: An Ensemble-Based Regressive Neighborhood Model 197\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYuvaraj Duraisamy, Salar Faisal Noori and Shakir Mahoomed Abas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 197\u003c\/p\u003e \u003cp\u003e12.2 Related Works 200\u003c\/p\u003e \u003cp\u003e12.3 Methodology 200\u003c\/p\u003e \u003cp\u003e12.3.1 Pre-Processing 200\u003c\/p\u003e \u003cp\u003e12.3.2 Feature Selection 202\u003c\/p\u003e \u003cp\u003e12.3.3 Classification 202\u003c\/p\u003e \u003cp\u003e12.4 Numerical Results and Discussion 203\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 206\u003c\/p\u003e \u003cp\u003eReferences 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Detection of IoT Attacks Using Hybrid RNN-DBN Model 209\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePavithra D., Bharathraj R., Poovizhi P., Libitharan K. and Nivetha V.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 210\u003c\/p\u003e \u003cp\u003e13.2 Related Work 212\u003c\/p\u003e \u003cp\u003e13.3 Methodology 216\u003c\/p\u003e \u003cp\u003e13.3.1 Dataset Used 216\u003c\/p\u003e \u003cp\u003e13.3.2 Data Preprocessing 217\u003c\/p\u003e \u003cp\u003e13.3.3 Data Normalization 217\u003c\/p\u003e \u003cp\u003e13.3.4 Multi-Class Classification 218\u003c\/p\u003e \u003cp\u003e13.3.5 Splitting Dataset 219\u003c\/p\u003e \u003cp\u003e13.3.6 RNN-DBN 219\u003c\/p\u003e \u003cp\u003e13.4 Experiments and Results 221\u003c\/p\u003e \u003cp\u003e13.5 Conclusion and Future Scope 224\u003c\/p\u003e \u003cp\u003eReferences 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Identification of Foliar Pathologies in Apple Foliage Utilizing Advanced Deep Learning Techniques 227\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTamal Kumar Kundu, Smritilekha Das and R. Nidhya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 228\u003c\/p\u003e \u003cp\u003e14.2 Literature Survey 229\u003c\/p\u003e \u003cp\u003e14.2.1 Disease Detection Using Machine and Deep Learning Techniques (2015–2021) 229\u003c\/p\u003e \u003cp\u003e14.2.2 Disease Detection Using Transfer Learning (2015–2021) 232\u003c\/p\u003e \u003cp\u003e14.3 Different Diseases of Leaves 233\u003c\/p\u003e \u003cp\u003e14.4 Dataset 236\u003c\/p\u003e \u003cp\u003e14.5 Proposed Methodology 239\u003c\/p\u003e \u003cp\u003e14.6 Data Analysis 240\u003c\/p\u003e \u003cp\u003e14.7 Pre-Processing Technique 241\u003c\/p\u003e \u003cp\u003e14.8 Data Visualization 242\u003c\/p\u003e \u003cp\u003e14.9 Evolutionary Progression and Genesis of Model 242\u003c\/p\u003e \u003cp\u003e14.9.1 Evolution Model 243\u003c\/p\u003e \u003cp\u003e14.9.2 Model Performance 244\u003c\/p\u003e \u003cp\u003eReferences 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Enhancing Cloud Security Through AI-Driven Intrusion Detection Utilizing Deep Learning Methods and Autoencoder Technology 249\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eP.V. Sivarambabu, Richa Agrawal, Arepalli Tirumala, Shaik Mahaboob Subani, Veeraswamy Parisae and S. V. L. Sowjanya Nukala\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 250\u003c\/p\u003e \u003cp\u003e15.2 Related Work 251\u003c\/p\u003e \u003cp\u003e15.3 Proposed Methodology 253\u003c\/p\u003e \u003cp\u003e15.3.1 DL-Based IDS for Cloud Security 253\u003c\/p\u003e \u003cp\u003e15.4 Results and Discussion 254\u003c\/p\u003e \u003cp\u003e15.4.1 Performance Analysis 258\u003c\/p\u003e \u003cp\u003e15.4.1.1 Accuracy 259\u003c\/p\u003e \u003cp\u003e15.4.1.2 Precision 260\u003c\/p\u003e \u003cp\u003e15.4.1.3 Recall 260\u003c\/p\u003e \u003cp\u003e15.4.1.4 F1 Score 261\u003c\/p\u003e \u003cp\u003e15.4.1.5 AUC-Area Under the Curve 261\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 262\u003c\/p\u003e \u003cp\u003eReferences 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 YouTube Comment Analysis Using LSTM Model 265\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePavithra D., Poovizhi P., Rokeshkumar G., Bharathvaj T. and Mageshkumar M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 266\u003c\/p\u003e \u003cp\u003e16.2 Related Work 266\u003c\/p\u003e \u003cp\u003e16.3 Literature Survey 267\u003c\/p\u003e \u003cp\u003e16.4 Existing System 272\u003c\/p\u003e \u003cp\u003e16.5 Methodology 273\u003c\/p\u003e \u003cp\u003e16.6 Result and Discussion 275\u003c\/p\u003e \u003cp\u003e16.7 Conclusion 280\u003c\/p\u003e \u003cp\u003eReferences 280\u003c\/p\u003e \u003cp\u003eIndex 283\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433206706456,"sku":"9781394209224","price":144.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394209224.jpg?v=1784851822","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/generative-artificial-intelligence-concepts-and-applications-hardback-9781394209224","provider":"Freshly Printed Books","version":"1.0","type":"link"}